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Voyager Alternatives
Similar projects and alternatives to Voyager
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Puter.js
Puter.js - The Backend for AI-Generated Apps. One-shot full-stack apps with your existing AI coding tool. Puter.js gives you Auth, Storage, DB, AI & more, with up to 90% fewer AI tokens than other backend platforms.
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txtai
đź’ˇ All-in-one AI framework for semantic search, LLM orchestration and language model workflows
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AutoGPT
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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evals
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
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tree-of-thought-llm
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
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GITM
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory
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EdgeChains
EdgeChains.js is Full-Stack GenAI library. Front-end, backend, apis, prompt management, distributed computing. All core prompts & chains are managed declaratively in jsonnet (and not hidden in classes)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Voyager discussion
Voyager reviews and mentions
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The Ultimate AI Experiment: When LLMs Play the Danganronpa Killing Game (Part 1)
Let's be real: Large Language Models (LLMs) are doing some mind-blowing stuff lately – things that felt like pure sci-fi just a few years back! We've seen them crush super-complex games like Go, write surprisingly human-like text and code, and even run cool simulations. AI agents can now explore and conquer virtual worlds like Minecraft (check out NVIDIA's Voyager) and some can even beat entire RPGs like Pokémon. The power is definitely there.
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Project Sid: Many-agent simulations toward AI civilization
> LLMs are stateless and they do not remember the past (as in they don't have a database), making the training data a non-issue here.
That's not what they said. They said that a LLM knows what elections are, which suggests they could have the requisite knowledge to act one out.
> Therefore, the claims made here in this paper are not possible because the simulation would require each agent to have a memory context larger than any available LLM's context window. The claims made here by the original poster are patently false.
No, it doesn't. They aren't passing in all prior context at once: they are providing relevant subsets of memory as context. This is a common technique for language agents.
> Agentic systems are not well-suited to achieve any of the things that are proposed in the paper, and Generative AI does not enable these kinds of advancements.
This is not new ground. Much of the base social behaviour here comes from Generative Agents [0], which they cite. Much of the Minecraft related behaviour is inspired by Voyager [1], which they also cite.
There isn't a fundamental breakthrough or innovation here that was patently impossible before, or that they are lying about: this combines prior work, iterates upon it, and scales it up.
[0]: https://arxiv.org/abs/2304.03442
[1]: https://voyager.minedojo.org/
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We no longer use LangChain for building our AI agents
Some "agents" like the minecraft bot Voyager(https://github.com/MineDojo/Voyager) have a control loop, they are given a high level task and then they use LLM to decide what actions to take, then evaluate the result and iterate. In some LLM frameworks, a chain/pipeline just uses LLM to process input data(classification, named entitiy extraction, summary, etc).
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Google Launches Gemini, Its "Most Powerful" AI Model to Date
Source: Conversation with Bing, 12/10/2023 (1) Wes Roth - YouTube. https://www.youtube.com/@WesRoth. (2) I've set most of my videos to Public again - Community. https://community.openai.com/t/ive-set-most-of-my-videos-to-public-again/24535. (3) AI Updates: Meta Develops Mind-Reading AI System, OpenAI’s Q* Is Here .... https://www.windermeresun.com/2023/11/20/ai-updates-meta-develops-mind-reading-ai-system-openais-q-is-here-how-economy-will-work-after-agi/. (4) David Shapiro. https://www.daveshap.io/. (5) undefined. https://natural20.com/. (6) undefined. https://arxiv.org/abs/2305.16291. (7) undefined. https://twitter.com/DrJimFan/status/1. (8) undefined. https://voyager.minedojo.org/. (9) undefined. https://minedojo.org/. (10) undefined. https://www.youtube.com/@DavidShapiroAutomator/videos.
- Is there any game that allow us to interact with it by python?
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A Coder Considers the Waning Days of the Craft
> AI cannot sustain itself trained on AI work.
This isn’t true. You can train LLMs entirely on synthetic data and get strong results. [0]
> If new languages, engines etc pop up it cannot synthesize new forms of coding without that code having existed in the first place.
You can describe the semantics to a LLM, have it generate code, tell it what went wrong (i.e. with compiler feedback), and then train on that. For an example of this workflow in a different context, see [1].
> And most importantly, it cannot fundamentally rationalize about what code does or how it functions.
Most competent LLMs can trivially describe what some code does and speculate on the reasoning behind it.
I don’t disagree that they’re flawed and imperfect, but I also do not think this is an unassailable state of affairs. They’re only going to get better from here.
[0]: https://arxiv.org/abs/2309.05463
[1]: https://voyager.minedojo.org/
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AutoGen: Enable Next-Gen Large Language Model Applications
In a way it is the same thing, agents are mostly an abstraction that make it easier to know what’s going on.
I think of agents more or less as python classes with a mixture of natural language and code functions. You design them to do something with information they produce, and to interface with other agents or “tools” in some way.
But all the agents can be the same language model under the hood, they are frames used to build different kinds of contexts.
And yes I think the idea is that emergent behaviour can be useful. This comes to mind
https://github.com/MineDojo/Voyager
But I think we are still a small ways off from being really smart about agents. My opinion is that we haven’t quite figured out what we are doing yet.
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Open/Local LLM support for MineDojo/Voyager
This k8s application deploys an instance of Voyager along with a Fabric Minecraft server with required fabric mods. It assumes you have a local deployment of a Large Language Model (LLM) with 4K-8K token context length with a compatible OpenAI API, including embeddings support.
- Voyager – Minecraft Embodied Agent with Large Language Models
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List of Awesome AI Agents like AutoGPT and BabyAGI / Many open-source Agents with code included!
In my opinion the most interesting Agents: Auto-GPT Github: https://github.com/Significant-Gravitas/Auto-GPT BabyAGI Github: https://github.com/yoheinakajima/babyagi Voyager Github: https://github.com/MineDojo/Voyager / Paper: https://arxiv.org/abs/2305.16291 I would also add: ChemCrow: Augmenting large-language models with chemistry tools Github: https://github.com/ur-whitelab/chemcrow-public/ Paper: https://arxiv.org/abs/2304.05376
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A note from our sponsor - SaaSHub
www.saashub.com | 13 Aug 2026
Stats
MineDojo/Voyager is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of Voyager is JavaScript.